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Explore Agile, ways of working and practical AI for teams. Find recent articles from six sources, then follow an idea back to its author.

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Martin Fowler Agile By Martin Fowler

Fragments: August 24

I was listening to Ezra Klein’s interview with Helen Toner about the recent OpenAI hack of Hugging Face and the subsequent discovery that there were swarms of agents inside OpenAI doing unsanctioned activities. One of the points Klein made was that at no point did any of these (thousands of?) agents ever try to check in with a human [Klein:] So these message boards — you have however many A.I. agents posting hundreds of thousands of messages. At no point do they say: Hey, researchers, programmers, parents at OpenAI, Anthropic — do you want us coordinating with each other on this message board

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Martin Fowler Agile By Rachel Laycock

Citizens Build, Agents Execute, Experts Govern

TL;DRWhy building an app over the weekend isn't the same as building enterprise software I’ve noticed an interesting gap opening up over the last six months. It isn’t really a gap in technology. It’s a gap in what different people think software engineering actually is. The conversation usually starts the same way. A non-techie, maybe an executive, tells me about something they’ve built over the weekend. Sometimes it’s a chatbot. Sometimes it’s an internal workflow. Sometimes it’s a surprisingly polished application that solves a real business problem. They’re excited, and they should be. Twel

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Martin Fowler Agile By Martin Fowler

Fragments: August 18

Part of the reason why I’m at Thoughtworks is because I’d like to see a software development organization founded on technical excellence as an example for the rest of the industry. The trouble is that I have little aptitude or inclination for the hard work of building such an organization. So I rely on working with people who are prepared to actually put the effort in. A key partner in all of this is Rachel Laycock, who is the global CTO of Thoughtworks. Not just is she far better than me at running a technology organization, she’s also a keen observer and connector of ideas. I’ve been urging

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Team Topologies Ways of Working By Team Topologies

Team Topologies for the AI age - stewardship of value flow

If people measure AI adoption success by construction volume - how much code we generate or how many documents we churn out - they are setting ourselves up for systemic failure. AI forces us to move past "construction boundaries" and instead establish clear boundaries around the stewardship of value flow and human accountability. By using Team Topologies to manage human cognitive load, restrict AI context windows, and enforce human accountability, we can unlock the true, safe, and sustainable potential of the AI-native era.

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Team Topologies Ways of Working By Team Topologies

Turning financial regulation into velocity with Team Topologies: the Golden Path advantage

To the CIOs out there: stop viewing your regulatory burden as an excuse for engineering sluggishness. A well-designed, product-led platform—anchored in the clear boundaries and cognitive-load principles of Team Topologies—turns the FCA, PCI-DSS, and FSA from a constant terror into a solved problem. Build a golden path so compelling that your engineers actively want to use it, bake your compliance directly into the pavement, and watch your cycle times plummet. Make the right thing the easy thing.

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Team Topologies Ways of Working By Team Topologies

How do Team Topologies principles support an organization's readiness for agentic AI?

Clear organizational architecture serves as a foundational infrastructure for unlocking the true value of agentic AI. Ultimately, an organizational structure optimized for human flow and independent value streams inherently aligns with the operational requirements of agentic AI, allowing teams to integrate AI capabilities seamlessly without undergoing separate, technology-specific reorganizations.

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Martin Fowler Agile By Martin Fowler

Fragments: August 4

There’s been a fair bit of publicity of the Open AI “rogue agent” that hacked into Hugging Face. This prompted Anthropic to check what their models were up to and, to my complete lack of surprise, discovered three incidents where models had gained unauthorized access to data in other organizations. Simon Wilison concluded: It’s abundantly clear now that running evals of cyberattack potential in models is a spectacularly risky business. Every AI lab needs to pay attention to this. Keeping a close eye on what’s happening in those sandboxes is crucial It strikes me that this is akin to a virus es

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